System and method for topic extraction and opinion mining

ABSTRACT

Techniques for topic extraction and opinion mining are described. For example, a machine accesses a document from a document record of a database. The machine builds a syntax tree of a sentence of the document based on parsing the sentence. The machine assigns a polarity impact to one or more words of the sentence. The machine determines that a plurality of words in the sentence have conflicting polarity based on the syntax tree and the polarity impact of the plurality of words. The machine determines a classification of the sentence based on the determining that the plurality of words in the sentence have conflicting polarity. The machine generates a classification record of the classification in the database.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims the benefit of priorityunder 35 U.S.C. § 120 to U.S. patent application Ser. No. 12/568,583,now U.S. Pat. No. 8,533,208, entitled “SYSTEM AND METHOD FOR TOPICEXTRACTION AND OPINION MINING,” filed on Sep. 28, 2009, and to U.S.patent application Ser. No. 14/020,421, entitled “SYSTEM AND METHOD FORTOPIC EXTRACTION AND OPINION MINING,” filed on Sep. 6, 2013, which arehereby incorporated by reference herein in their entirety.

BACKGROUND

Electronic transactions provides a convenient mechanism for sellers andbuyers to transact business. Communications related to such services maybe received from users by way of a community forum or other feedbackmechanism, and are recorded and stored in databases and session logs.This information is accessed to determine the performance of productsand advertisements, as well as the performance of sellers.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 and 2 are block diagrams illustrating a system having aclient-server architecture to provide services and facilitatecommunications, according to an example embodiment;

FIG. 3 is a block diagram illustrating a system and apparatus toimplement topic extraction and sentiment analysis of communicationswithin a system as illustrated in FIG. 1, according to an exampleembodiment;

FIGS. 4, 5 and 6 illustrate, in flow diagram form, topic extraction andsentiment analysis, according to an example embodiment;

FIG. 7 illustrates tokens used to extract topics from texts, accordingto an example embodiment;

FIG. 8 illustrates special tokens to identify tasks used to extracttopics from texts, according to an example embodiment;

FIGS. 9 and 10 illustrate patterns formed by multiple tokens used toextract topics from texts, according to example embodiments;

FIG. 11 illustrates computer code to implement a topic extraction andsentiment analysis method, according to an example embodiment; and

FIG. 12 is a block diagram of a system to extract topics and analyzesentiment of texts, according to an example embodiment.

FIG. 13 is a graphical user interface reporting topic extraction andsentiment analysis, according to an example embodiment.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of some example embodiments. It may be evident, however,to one of ordinary skill in the art that embodiments of the inventionmay be practiced without these specific details.

For services offered through a networked communication system, such asan on-line service offered over the Internet, suppliers of products andservices coordinate with consumers. Users of the system often providecomments and feedback, which is then available to buyers, sellers, andothers. Often information relating to a product, service or participantis entered in feedback and community forums, and includes significantinformation related to sentiment polarity and opinion. Comments relatedto a specific topic may be entered as comments in multiple forums,venues or locations. For example, comments relating to a digital cameramay be entered in a community forum related to photography, a communityforum related to electronics, a review database for a seller, a productreview section, and others. When seeking information related to thedigital camera, a user may be presented with a multitude of reviews,comments, notes and so forth, requiring the user to manually scanthrough and read the individual reviews. This may become burdensome withthe popularity of a product.

Sellers, marketers and others seek feedback and customer opinion as toproducts and services. Potential buyers also seek this information. Inpractice, many of these community forums receive a great volume ofentries, making identification of desired information difficult, as asearch of these entries requires the user to manually read through thecomments and messages.

The following description details methods for topic extraction andopinion mining in an electronic system, including the application ofpolarity detection to identify words and phrases most likely related tosentiment, and further to apply lexical patterns to such words andphrases to understand the use of the polarity words and phrases so as toevaluate the significance of comments related to a product, service,participant or market. The communications may include comments on acommunity forum or feedback board, comments solicited from users of aservice or product, or selections made on a questionnaire. Thecommunications may convey opinion or sentiment of a user. The processesdiscussed herein extract the opinion and sentiment informationautomatically, avoiding manual searching of the communications.

The information is then presented in a format for users to evaluate andmay be implemented into a decision making process. In one example, theresultant information is presented graphically to identify trends. Theinformation may further be used to generate ratios of positive feedbackto negative feedback. In some examples, the information is automaticallyevaluated and presented to a requester as an alarm or indicator when theresultant information satisfies specified criteria. In some examples theresultant information is compared to information related to otherqueries, such as to compare results for one product against results fora similar or competing product.

In one embodiment, a topic and opinion mining method is determined frommultiple community boards. The method may include techniques for featureextraction, such as Latent Dirichlet Allocation (LDA), to extract topicsfrom user community boards. The topics are identified by key words andphrases, which are then classified as positive or negative comments. Themethod then analyzes the classified information to identify and confirmsentiment information in an automatic manner. Such a method discoverstopics from diverse opinion pages and classifies the topics usingbusiness judgment to identify polarity of the comments in the textincluding key phrases as well as the community reaction to productlaunches, and other initiatives.

In an example embodiment, a system includes two main components: i)topic extraction; and ii) opinion mining, wherein topic extractionincludes a first phase to extract key phrases from texts and documentsin the community forum or other venue and a second phase of opinionmining to analyze the sentiment of sentences including the key phrases,and wherein the opinion mining includes syntactical analysis and lexicalpattern matching. The topics may be ranked to identify essential topics.Automatic identification of essential topics in a given document corpusis a challenging task as words may be used in various contexts, and thecorpus is a large set of texts or documents which are used to performstatistical analysis.

In one embodiment, a natural language process is used to identify keyphrases related to the topic of interest among the various documents.Further, such processing may apply a machine learning method to extractkey phrases covered in the discussion posts and other documents. Once agroup of essential ranking documents is identified, the method applies aclustering technique to the group of documents, which infers arelationship(s) among topics that belong to that group.

One example embodiment of a distributed network implementing imagerecognition services for identifying data items stored in an informationresource is illustrated in the network diagram of FIG. 1, which is ablock diagram illustrating a system 10 having a client-serverarchitecture and for providing image services, according to an exampleembodiment. Within system 10, a commerce platform or commerce serverincludes an information storage and retrieval platform 12, whichprovides server-side functionality, via a network 14 (e.g., theInternet) to one or more clients. As illustrated, system 10 interactswith a web client 16 executing on a client machine 20, a programmaticclient 18 executing on a client machine 22, and a programmatic client 8in the form of modules 5 executing on a client machine 23. In oneembodiment, the web client 16 comprises a web browser, but may employother types of web services.

An electronic commerce service may run from one or multiple of theclient machines, such as 20, 22 and 23, which may be provided at acommon location or a variety of separate locations. Service providersand users may interface with the topic extraction and sentiment analysismodule 32, which accesses information stored in a database 36, messagesand texts 37, session logs 4, and dictionaries 6 within informationstorage and retrieval platform 12. The session logs 4 include listingsof information from user sessions interacting with a service or product.The session logs 4 may organize the information so as to coordinate thisinformation with the messages and texts 37. For example, when a messageor comment includes text relating to a digital camera, the correspondingsession logs may provide information relating to the specific digitalcamera purchased, the software purchased, or other information which maybe used to better understand the comment. The messages and texts 37 mayinclude the full text of the messages or may be a summary of informationsubmitted in response to a questionnaire. The dictionaries 6 may includea variety of information, including polarity dictionaries to identifywords and phrases related to sentiment and opinion, such as “good” or“poor.” The dictionaries 6 may include lexical information dictionaries,and other resources used for topic extraction and sentiment analysis.

Within the information storage and retrieval platform 12, ApplicationProgram Interface (API) server 24 and web server 26 are coupled to, andprovide programmatic and web interface to, one or more applicationservers 28. Application servers 28 host one or more modules 30 (e.g.,applications, engines, etc.), further detailed in FIG. 2. Applicationservers 28 are, in turn, illustrated as coupled to one or more databaseservers 34 that facilitate access to one or more databases 36. Modules30 provide a number of information storage and retrieval functions andservices to users accessing the information storage and retrievalplatform 12. A user accesses information storage and retrieval platform12 through network 14 such as from a computing device.

While system 10 of FIG. 1 employs a client-server architecture, thepresent disclosure is not limited to this architecture, and could beapplied to a distributed, or peer-to-peer architecture system. Thevarious modules 30 may also be implemented as stand-alone softwareprograms, which do not necessarily have networking capabilities.

The web client 16 may access the various modules 30 via a web interfacesupported by web server 26. Web server 26 allows developers to build webpages. In one embodiment, web server 26 may be used in collaborationwith JAVA® technologies by Sun Microsystems of Menlo Park, Calif., andwith Ajax (Asynchronous JavaScript and XML) technologies, whichcomprises a collection of technologies enabling the creation of webapplications. Ajax uses JavaScript, eXtensible Markup Language (XML),and Cascading Style Sheet (CSS) formatting, along with othertechnologies. Ajax allows programmers to refresh certain parts of a webpage without having to completely reload the page. By obtaininginformation dynamically, web pages load faster, respond more quickly torequests, and are more functional. Developers consider using Ajaxapplications, and Ajax-like applications, when seeking to reduce networklatency in certain applications.

Similarly, programmatic client 18 accesses various services andfunctions provided by the modules 30 via the programmatic interfaceprovided by the API server 24. In one example, programmatic client 18comprises a seller application (e.g., the TURBOLISTER® applicationdeveloped by eBay Inc., of San Jose, Calif.) enabling sellers to authorand manage data item listings, such as where each listing corresponds toa product or products, on information storage and retrieval platform 12.Listings may be authored and modified in an off-line manner such as whena client machine 20, 22, or 23 is not necessarily connected toinformation storage and retrieval platform 12. Client machines 20, 22and 23 are further to perform batch-mode communications betweenprogrammatic clients 18 and 25 and information storage and retrievalplatform 12. In addition, programmatic client 18 and web client 16 mayinclude authoring modules (not shown) to author, generate, analyze, andpublish categorization rules used in information storage and retrievalplatform 12 to structure data items and transform queries. In oneexample embodiment transforming queries uses a data dictionary withtoken pairs to expand a narrow keyword or to focus a broad keyword. Theclient machine 23 is coupled to one or more databases 27. The databases27 include information used by client machine 23 in implementing aservice or operation and may include specific information for productsor services offered by client machine 23.

Users having access to service(s) provided by client machine 23, forexample, include users of computer 19 and users of wireless network 17,which may serve as a common access point to network 14 for a variety ofwireless devices, including, among others, a cable-type televisionservice 11, a Personal Digital Assistant (PDA) 13, and a cellular phone15.

In one example, client machines 20, 22 and 23 offer services which storeat least some information in the information and storage retrievalplatform 12 to enable web services. The services may provide users witha mechanism to provide feedback and other information to the service byway of community forums, feedback board, or other entries. Theinformation received from users may be stored in a database, such as amessages and texts 37 within the information storage and retrievalplatform 12.

In some embodiment, a catalog of web services comprises informationstored in the information storage and retrieval platform 12. Clientmachine 23 stores information related to use of the web services indatabases 27, wherein the information may be used to identify associatedservices and offerings. The associated services and offerings are alsolisted in the catalog of web services. Descriptors of the associatedservices and offerings may be used to generate and modify a vocabularyfor a data dictionary corresponding to the catalog of web services, suchthat a user search having keywords related to a first service may returnresults for a second service associated with the first service.Additionally, each of client machines 20, 22 and 23 may also be usersthat search data items in information storage and retrieval platform 12.

In another example, client machine 23 may be a data processing clientoffering products to customers via network 14. Client machine 23 storesa catalog of products in information storage and retrieval platform 12,with the catalog of products having a corresponding data dictionary.Client machine 23 stores information related to at least one product indatabases 27 or dictionaries 6. The information may also includefrequency of searches, resultant sales, related products, pricinginformation, and other information related to customer use of the dataprocessing service. Additionally, databases 27 may store otherproduct-related information, such as style, color, format, and so forth.Client machine 23 may use the information stored in databases 27 todevelop descriptor information for at least one product and to determinethe popularity or sentiment related to the product. Similarly, a serviceprovider or seller may use the sentiment information to developadvertising and marketing information. The sentiment information may beused to automatically enhance the service provider's business andelectronic service. Product descriptors and other product informationmay be used to generate and modify a vocabulary for a data dictionarycorresponding to the catalog of products, such that a user search havingkeywords related to a first product may return results for a secondproduct associated with the first service. In other embodiments, aclient machine, such as client machines 23, 22 and 20, may storeinformation in the information and storage retrieval platform 12 relatedto business processes, or other applications which store data in adatabase which may be accessed by multiple users.

Continuing with system 10 of FIG. 1, information storage and retrievalplatform 12 includes modules 30 within application server(s) 28, whereinmodules 30 are further detailed in FIG. 2. Similar to the illustrationof FIG. 1, FIG. 2 is a block diagram illustrating modules 30 withinsystem 10 according to an example embodiment. The modules 30 may includesoftware modules or the functionality of a module implemented at leastpartially in software. The software may be developed using a programminglanguage, such as JAVA, which is an object-oriented programming languagedeveloped by Sun Microsystems. Other languages and development tools maybe used according to the design and purpose and at the discretion of thesystem developer.

Modules 30 are to receive images and other information from entitieswithin system 10, such as through network 14 (see FIG. 1). Furthermodules 30 comprises a communication module 21, to receive, process andtransmit messages according to one or multiple communication protocols.Processing modules 44 are used to process requests and interface withthe various modules and functions of the information storage andretrieval platform 12. Similarly, marketplace applications 46, and APIs48 are used to interface for various services within the system 10. Theecommerce service may further publish information using the publishmodules 40 including a query engine 50, a search index engine 60 and acategorization service engine 70. A service provider or user mayincorporate the information available through the publish modules 40 toenhance topic extraction, such as to identify keywords and phrasesrelated to a product or service. Still further, the sentiment analysismay incorporate the publish modules 40 to better understand comments andtext related to services and products.

The query engine 50 includes categorization information 52, a metadataservice module 54, and metadata information 56, which stores themetadata information. The search index engine 60 includes search indexes62, data item search information 64, and publish modules 66. Thecategorization service engine 70 includes the categorization information72.

The tools 49 provide developer tools and software for buildingapplications, such as to expand or enhance the image processingcapabilities. In one example, tools 49 include Java servlets or otherprograms to run on a server. As the present example implements Javatools, some terms used with respect to Java applications and tools aredetailed. A Java applet is a small program sent as a separate file alongwith an HTML communication, such as a web page. Java applets are oftenintended to run on a client machine and enable services. Java appletservices, for example, may perform calculations, position an image inresponse to user interaction, process data, and so forth.

As illustrated in FIG. 1, the information storage and retrieval platform12 further includes a topic extraction and sentiment analysis module 32,which in one embodiment includes instructions for executing a method foridentifying topics in texts and documents to determine opinions andsentiment related to products and services. The topic extraction andsentiment analysis module 32 of FIG. 1 may further be described as inFIG. 3, which is a block diagram illustrating a system 200 and apparatusfor implementing a topic extraction method, according to an exampleembodiment. The system 200 extracts topic information by identifying keyphrases in documents and other texts, and then ranks the key phrasesaccording to import of the key phrases in identifying sentiment oropinion in the document or text. Finally, the system clusters or groupsdocuments so as to infer relationships among topics belonging to eachgroup.

The target of topic extraction is a set of documents within a given setor corpus. A document as used herein refers to information in a textualform, such as comments submitted to a community forum. Service providersmay provide a forum or board which allows postings of comments,feedback, questions and other information. A topic is a concept,expressed either in single words or multi-word phrases, representing aconcept or idea for a set of documents. In some examples the topic mayrepresent ideas substantially related to the documents, such as to thecontent of the documents, type of documents, or title of documents. Inone example, “Nikon D420” is a topic that represents documents whichdiscuss Nikon D420 digital cameras. In some embodiments, documents mayinclude entries submitted to a community forum, emails provided withrespect to a topic, and other information available and related to atopic. Other information may include forum posts, comments, feedback,user emails, blog entries, question and answer entries, and so forth.The system 200 identifies information related to a specific topic, suchas a digital camera, and from this information determines opinions andother sentiment related to the topic. The topic may be broadly defined,and may include multiple subtopics. The topics may be computed andselected using a combination of multiple methods, including TermFrequency—Inverse Document Frequency (TFIDF), Mutual Information (MI),Latent Dirichlet Allocation (LDA) and others. The topics identified arethen stored in a database table for further use.

As illustrated, a variety of forum documents 201 are provided as inputsto the topic extractor 202, which includes a post index module 204, atopic extraction module 206 and optional additional filters 208. Sessionlogs 210 may also provide documents containing messages related to aspecific topic. The text extractor outputs relevant documents, e.g.,documents relevant to the topic. The relevant documents are thenprovided to the sentiment analyzer 222. The relevant documents mayinclude the complete documents or may be sentences or portions of textfrom the documents which include key phrases related to the topic. Thepost index module 204 is used to index and organize the variousdocuments within a community forum or other document corpus. Theorganization puts the documents in an order to facilitate searching andother analysis. The post index module 204 records and indexes the numberof each post or document for topic-document reference and retrieval. Thepost index module 204 outputs an index that contains a mapping of eachpost number to all constituent word IDs and an inverted index of eachindividual word to membership post numbers.

The topic extraction module 206 then receives the indexed documents andsearches to identify key phrases within the documents. Additionalfilters 208 may use heuristic or other information to identify therelevant documents and messages, for example a filter may be used toextract sentences or longer phrases from the documents. The relevantdocuments 220 are then output from the topic extractor, wherein therelevant documents 220 may be identified as complete documents, or maybe portions of documents. In some embodiments, each of the relevantdocuments 220 includes an identifier of the specific community forum orvenue of which the relevant document 220 was part. Operation of thevarious modules of the topic extractor 202 is provided in the flowdiagram of FIG. The relevant documents 202 may be a list of documents toidentify an access location. Similarly, the relevant documents 202 maystore the relevant portions of documents related to the topic.

The text from the relevant documents 220 are then processed by thesentiment analyzer 222 to evaluate the words, sentences, and phrasesthat include sentiment indicators which allows the words, sentences, andphrases be classified as “positive” or “negative” sentiment. Theresultant classification is used to understand opinions and expressionsof sentiment about the topic. To this end, a polarity dictionary 230 maybe used to identify specific polarity words, such as “good” or“horrible.” The sentiment analyzer 222 includes a polarity detectionunit 224, used with the polarity dictionary 230, to identify key phraseswhich indicate a sentiment or opinion. In one example the polarityidentifies positive or negative comments. However, in some embodiments,other sentiments may be identified as well, such as informationalcomments. For example, an informational comment may include feedbackfrom users as to the type of photographs taken with a particular digitalcamera. Other types of comments may be identified as well, wherein adictionary of words associated with the classification criterion arestored. The polarity detection unit 224 identifies positive and negativecomments using a polarity word dictionary 230, and then provides thosedocuments containing the positive or negative comments.

A syntactic parser 226 receives the polarity information from thepolarity detection unit 224, and applies a syntactic parsing operationto the received information. The syntactic parser 226 may be used tobuild syntactic tree of a sentence or portion of text, and may applyheuristic rules to identify or filter particular portions of thesentence or portion of text. The syntactic parser 226 receives the textthat must be analyzed as a set of sentences or strings. It mainlyincludes word tokenization, part of speech tagging and phrase chunkingand phrase relation recognition components. Finally, a sentence isrepresented as a syntactic tree structure. The results from thesyntactic parser 226 are applied to a lexical pattern matcher 228. Thesentiment analyzer 222, and modules therein, may access informationstored in files relating to the topic, such as a file 203 of topics andopinions. The polarity detection unit 224 further uses information froma lexical dictionary 240, which includes terms organized and groupedaccording to relationships of synonyms and so forth. Operation of thesentiment analyzer 222 is detailed in FIG. 5. The polarity detectionunit 224 uses a lexical dictionary containing a set of words with anassociated integer (+) or (−) representing its polarity. A sentimentexpression may be a combination between polarity words and lexicalwords. For example, the lexical words, “anymore,” “at all,” “again,”“any longer” may show negative meaning when following “not do . . . ,”although they are not necessarily polarity words. So we will group thesesynonyms and generate one pattern.

FIG. 4 is a flow diagram illustrating operation 400 of the topicextractor 202 starting by identifying target document files at operation402. The documents may be specified as related to a product, service,issue, or other subject. The documents may be selected by a seller inorder to retrieve feedback and opinion information related to a productor service they are selling, or a feature of their business, such asresponse to a new website feature, and so forth. The documents may beinput into the system 200 by way of a field in a user interface, or maybe selected from a list. The documents may include community forums anddocument venues which may be used to search for opinion information. Atopic extractor 202 continues to search the group of documents toidentify key phrases within the text of the group of documents atoperation 404. In some embodiments, the process also identifies keyphrases or other information associated with the document, such asmetadata which may be used to classify and identify the source of eachdocument and so forth. Phrase extraction, such as for bigrams or phrasesbased on textual patterns, is applied before the topic extraction phraseso they can be provided as input to the LDA topic extraction algorithm.

In one embodiment, automatic key phrase extraction provides a tool foridentifying key words and phrases used in community forum entries,emails, and other documents related to a topic. Key phrases arelinguistic descriptors of textual content of documents and Key PhraseExtraction (KEA) is implemented to retrieve phrases from documents. Insome embodiments, the KEA method includes a natural language processingtool to find noun phrases and verb phrases automatically. In manytext-related applications techniques for clustering and summarizationalso may be used to identify phrases indicating a sentiment. A datamining or machine learning tool may be used to find multi-word phrasesor other parts of a text. A KEA process may include two stages, a firststage which builds a model based on training documents and a secondstage uses that model to predict the likelihood of each phrase in thenew given document. The first stage may include manually authored keyphrases, such as those submitted by a user looking for specific words orphrases. In one example, the system enables selection of a multi-wordconcept, such as “dropped calls.”

Continuing with FIG. 4, the text extractor 202 is further to apply aranking algorithm to key phrases to identify essential key phrases atoperation 406. In some embodiments, the ranking is done by TermFrequency-Inverse Document Frequency (TF-IDF) techniques where a weightis used to evaluate the importance, significance or relevance of a wordor phrase. A TF-IDF weight is a statistical measure used in informationretrieval and text mining. The TF-IDF weight indicates how important aword is within a document in a collection or corpus. In someembodiments, the importance increases proportionally to the number oftimes a word appears in a given document, which may be weighted againstthe frequency of the word in the corpus. Some embodiments implement aranking function that is computed as a function of the TF-IDF weights.Additionally, the KEA method may consider the first appearance of a wordor phrase in a text, such as the normalized distance of the firstappearance of a word to the beginning of the text. The KEA method isused to determine the lexical units in the text before the topicextraction. In some embodiments, an LDA process may then be used togenerate topics.

According to some embodiments, the phrases generated from the variousKEA methods are ranked as a function of weights applied by at least oneKEA method. The phrase rankings are evaluated with respect to athreshold, those phases having ranks that exceed the threshold areconsidered essential topics, at operation 408. The KEA based methods maybe used to generate key phrases and use this as input for LDA to improvethe grouping result. The list of essential topics is further extended toidentify associated sub-topics. In one example, the topics “new searchsystem” and “new search engine” are identified as subtopics of the “newsearch” topic.

Once the list of topics and subtopics are identified, the processassociates documents with corresponding topics at operation 410. For agiven topic, those documents in which the topic (e.g., essential keyphrase) appears are simply grouped together.

Various methods may be used to extend a document grouping, e.g., thosedocuments to which the topic is highly related are also grouped.Moreover, relationships may be extracted among topics that belong to thesame group. In a second stage, the KEA method then uses a model topredict the likelihood of each phrase in a new given document. Someexamples use a KEA method first to extract important phrases, and thenuse an LDA method incorporating the KEA results to improve the LDAresults by selecting good candidates for grouping at the very beginning.

The documents are then associated with the topic(s) based on theessential key phrases found in the documents at operation 410 andgrouped at operation 412 based on occurrence and use of key phrasesfound in the documents. The retrieved and grouped documents containingthe essential keywords are provided as relevant documents 220 of FIG. 3.

In some embodiments, some embodiments also use other filteringtechniques to identify and evaluate key phrases, including the use ofheuristics in key phrase extraction, such as capitalization,identification of non-stopwords which are filtered out prior due totheir common occurrence and other criteria, mutual information, andlength or number of characters in a phrase. The mutual information is aquantity that measures the mutual dependence of two variables, two wordsin this context. In text mining it is used to extract multi-word phrasesby identifying the words that appear together more often than by chance(word collocations) i.e “dropped calls”.

FIG. 5 illustrates operations of the sentiment analyzer 222 wherein apolarity dictionary 230 is used to identify words indicating sentimentin the essential key phrases at operation 502. In some embodiments thepolarity dictionary 230 is used in collaboration with a lexical databaseof English, such as the WordNet® dictionary by the Trustees of PrincetonUniversity, Princeton, N.J. Each word of a sentence is scanned over thepolarity dictionary 230 and the lexical database 240 to mark polaritywords and phrases.

The syntax analyzer 222 further builds a syntactic tree for eachsentence of the relevant documents that includes an essential key phraseat operation 504. A syntax tree is a tree that represents the syntacticstructure of a string according to a set of grammatical rules or norms.An example of a syntax tree includes multiple nodes identified as sourcenodes, leaf nodes or internal nodes, and terminal nodes. A parent nodehas a branch underneath the node, while a child node has at least onebranch directly above the node. The relationships are thus defined bybranches connecting the nodes. The tree structure shows therelationships among the various parts of a sentence.

In an example embodiment, building a syntax tree, such as performed atoperation 504, may incorporate a natural language parsing tool to obtaina syntactic tree of a target sentence. FIG. 6 further details operation504, wherein operation 602 parses the sentence, which includesactivities to tag parts-of-speech. The parsing may include detection ofsubjects and objects within the sentence, which information is used tobetter understand the use of words, terms, phrases and grammatical partsof the sentence structure. Additionally, parsing may involve detectionof negation words, such as “nor,” “not,” and “no.” For example, thenegation words may include “no trust,” “not trusted,” and “nor trusted.”The parsing may also include pronoun cross-reference, and otherinformation as to sentence structure.

The parsing allows the syntax analyzer 222 to build a syntax treatoperation 604, and assignment of polarity impact to individual words,phrases and terms at operation 606. For each of the polarity key phrasesincluded in the sentence, the impact assignment is a score identifyingthe impact of each polarity key phrase. The impact assignment may be afactor which indicates how much impact the polarity word is on the giventopic.

As an example, consider the sentence: “[The new search] is HORRIBLE ! !! Please dedicate your resources to improving another function of yourwebsite.” In this text, the there are two polarity words, “HORRIBLE” and“improving.” These two polarity words are in conflict as first word hasa negative meaning while the second word has a positive meaning. In thisexample, the word “horrible” is a stronger word and has more impact onthe given topic. The stronger impact may also reflect a direct relationwith the topic of “the new search.” Therefore, the entire text is to betagged as negative based on a comparison of the impact of theconflicting terms. The polarity impact assignment may be determined in avariety of ways. In one way, the polarity impact is considers thepolarity word having a dominant impact on the topic, and then uses thatword to determine the sentiment orientation of the topic. In anotherexample, the polarity impact may be determined by a sum of polaritiesmethod. Using the sum of polarities method, the example text will betagged as neutral. Positive words are assigned a +1 value and negativewords are assigned a −1 value. The sum of the polarities method adds upthe polarities of the words in a sentence. For each pair (w_(i),p_(i))in a sentence, where w_(i) is a word and p_(i) the correspondingpolarity. The sum is therefore the sum of all pi in the sentence. Theimpact score may also be detected using the syntactic distance betweenthe word and the topic in the syntactic tree. In other words, the numberof branches from a polarity word back up to the topic key phrasedetermines the impact of the polarity word. Still another method mayincorporate an impact score when adding up to all the polarity words.

At decision point 608 the syntactic analyzer 222 determines if there areany conflicting polarity words, and if so compares the polarity impactat operation 610. A polarity classification is made at operation 612,which may be positive, negative or neutral. Some embodiments have avariety of classifications indicating a degree of polarity.

At operation 614 heuristic rules are applied to the classified polaritywords and the text. These rules may handle special situations and usagepattern, such as negation, enantiosis and questioning. Negation wordsare those that tend to be related to negative sentiment, such as“nobody,” “null,” “never,” “neither,” “nor,” “rarely,” “seldom,”“hardly,” and “without,” in addition to the words given above. Followinga negation word, if the polarity word is close to the negation word andthere is no punctuation that separates the polarity word and thenegation word, then the significance of the polarity word is reversed.For example, in one scheme the word “liked” is assigned a polarity valueof +5, while the words “not liked” is assigned a reverse polarity of −5.In one example, the distance threshold is given as 5 words from thenegation word to the polarity word.

Additionally, the heuristic rules may evaluate figures of speech, suchas enantiosis, which affirmatively states a negation, or vice versa. Insome examples, question sentences may be skipped, as the meaning isambiguous. Consider the text “What is wrong with old Boolean search?”for which the author's sentiment is not easily discerned. Further, someexamples search for words in quotation marks, and skip those words asthe meaning is ambiguous. Consider the example text “IMO, this“improvement” is utterly ludicrous.” The meaning of the word“improvement” would typically be positive, however, in this context itis negative, and therefore, the process ignores the word. The heuristicsfor the topic extractor 202 are used to identify lexical units orphrases. These heuristics are used for sentiment analysis and may beexpressed using a common format or language, such as rules and patterns.

Continuing with FIG. 5, the method 500 further includes activities todetermine lexical patterns at operation 508 and then apply the lexicalpatterns at operation 510. A lexical pattern is defined as a tokensequence, where each individual token is an abstraction of a word. Atoken has three parts: a lemma of the word, a polarity tag and apart-of-speech tag. A lemma is a form of a word which identifies theword. The polarity tag identifies the word as having polaritysignificance. Examples are illustrated in FIG. 7, where a model of apolarity word token sequence is described by the token 700, including alemma field 702, a polarity tag field 704 and a part-of-speech field706. The polarity field 704 may identify the polarity as one of variouspossibilities: POS (possible), NEG (negative), NEU (neutral), and NOT(negation). The part-of-speech field 706 may include codes for thevarious parts-of-speech, such as NN for a noun, VB for a verb, JJ for anadjective, RB for an adverb, IN for a preposition, PRP for a possessiveterm, and CC for conjunction, and so forth.

In application, a wildcard may be implemented, such as to use “g.” In afirst example, a word is described by token 710, wherein the lemma field712 has an entry of “think,” and polarity and parts-of-speech fields 714and 716 have wildcard entries, which means the token 710 will identifyany text having the word “think” which is of any polarity and whereinthe word “think” may be any part-of-speech.

In a next example, the token 720 includes a wildcard in lemma field 722,but identifies a positive polarity in polarity field 724, and a verbidentifier in parts-of-speech field 726, meaning this token applies toany suitable positive verb In stilt another example, the token 730includes wildcards for lemma and parts-of-speech fields 732 and 736, anda negative entry in lemma field 734, meaning this applies to anynegative polarity words. The token 740 includes wildcard entries inlemma and polarity fields 742 and 744, and a noun identifier inparts-of-speech field 746, meaning this applies to any nouns. The token750 includes wildcards in polarity and parts-of-speech fields 754 and756, with an entry of the word “get” in lemma field 752, meaning this isone of the special words.

Embodiments may include a variety of words to identify theparts-of-speech broadly, using fewer terms, or narrowly, using moreterms. For example, the part-of-speech field 706 could include a codefor a verb in past tense and a second code for a verb in present tense.Or the part-of-speech 706 could include a single code for all verbs. Thesyntax analyzer 222 may further implement special tokens, such as thefeature token, or FEN, identifying a topic and the gap token, or GAP,identifying a number of words that may be skipped between two tokens.Examples of special tokens are illustrated in FIG. 8.

The token 800 includes the special token FEA in first field 802 andwildcards in the polarity and parts-of-speech fields 804 and 806,meaning the topic or key phrase of any polarity and used as anypart-of-speech. The token 810 has the special token GAP entered in field812, a zero is entered in field 814 and a three (3) in field 816. Thefield 816 indicates that three words may be skipped between two tokens.Tokens 814 and 816 may be used to indicate that up to 3 words (0 to 3words) may be skipped in between two tokens.

FIG. 9 illustrates an example pattern having tokens 900, 910, 920 and930. The token 900 includes a special token FEA in field 902, andwildcards in fields 904 and 906. The token 900 is to identify any textcontaining the topic or key phrase of any polarity and used as anypart-of-speech. In one scenario, the key phrase is “the new search,” andthe token 900 will identify any text containing the key phrase “the newsearch.” The token 912 includes a special token GAP in field 912. Thetokens 914 and 916 indicate that up to 3 words (0 to 3 words) may beskipped in between two tokens. In this way, the tokens 914 and 916identify the distance from the key phrase, and therefore the token 912identifies text within three (3) words of the key phrase. Further thetoken 920 includes a negation (NOT) in polarity field 924 with wildcardsin lemma and parts-of-speech fields 922 and 926, corresponding to anynegative polarity text. Additionally, the token 930 includes a text“work” in lemma field 932, with wildcard entries in polarity andparts-of-speech fields 934 and 936. The four tokens may be used togetherto identify a sentence including a key phrase and the word “NOT” withinthree words of the key phrase and the word “work,” In this scenario, thetokens 900, 910, 920 and 930 will identify the sentence “the new searchstill does not work, everyone on the planet knows it so why are we stillbeing forced to play this stupid game?”

FIG. 10 illustrates another example of a combination of tokens used toretrieve specific text. The tokens 1000, 1010 and 1020 are used toidentify text including the words “please” and “do,’ which has anegative polarity. The lemma field 1002 of token 1000 has an entry of“please,” while the other fields 1004 and 1006 have wildcard entries.Similarly, the lemma field 1012 of token 1010 has an entry of “do,”while the other fields 1014 and 1016 have wildcard entries. Finally, thetoken 1020 has a NOT entry in the polarity field 1024, with wildcards infields 1022 and 1024. In one scenario, the tokens 1000, 1010 and 1020identify the text “Please do not roll out this new search.”

Some other patterns defined by multiple tokens include: #_NOT_#(((want|think|use|need|believe|get)_#_#)(make_#_# sense_#_#))

which will identify or match with text such as the sentence “I do notwant Huge pictures and all the junkR like feedback and all the otherthings [the new search] brings up;” and (get|bring|give|put|change)_#_#GAP_0_3 FEA_#_# GAP_0_3 back_#_# which will identify or match with textsuch as the sentence “Put the Old Search and Browse back!!!!!”

The patterns may be coded into computer-readable instructions, such asillustrated in FIG. 11. The code 1100 is a pattern made up of severaltokens, wherein the pattern has definitions as defined in Table 1.

TABLE I Pattern Definitions P POSITIVE N NEGATIVE U NEUTRAL NT NEGATIONTPC TOPIC GP GAP

In one embodiment, a pattern is a list of pre-defined tokens and servesas a rule for determining the sentiment of a sentence. Each token is anindividual word or phrase. For each given sentence, the syntacticanalyzer builds a syntactic tree. If all the tokens in a rule may bematched in the syntactic tree, then the rule may be applied to thetarget sentence.

FIG. 12 illustrates a system for implementing a topic extraction andanalysis method according to an example embodiment. The system 1200includes a communication bus 1201, coupling the various units within thesystem 1200. A central processing unit 1218 controls operations withinthe system 1200 and is responsive to execute computer-readableinstructions for operations within the system 1200. A topic extractionunit 1214 is coupled to the interfaces 1206, which may include anApplication Programming Interface (API). The topic extraction unit 1214receives information and control information from a user via theinterfaces 1206. In some embodiments the interfaces 1206 are coupleddirectly to topic extraction unit 1214. The topic extraction unit 1214includes a topic extractor 1222, which is similar to topic extractor1202 of FIG. 3, and further includes the sentiment analyzer 1224, whichis similar to the sentiment analyzer 1222 of FIG. 3. The topic extractorunit 1214 receives information from the databases 1220 and memorystorage 1208 via the communication bus 1201. The databases 1220 includesa polarity dictionary 1210 having listings for a variety of words orphrases that are associated with polarity comments, such as words toindicate a negative aspect or a positive aspect of a product or service.The databases 1220 further includes a relevant documents store 1216,which identifies those documents which are determined as relevant basedon the keywords. The system 1200 performs the operations described withrespect to the various methods and apparatuses described herein.

The system 1200 further includes a receiver 1204 and a transmitter 1212to facilitate wireless communications. Some embodiments have no wirelesscapability.

FIG. 13 illustrates a graphical user interface for reporting topicextraction and sentiment analysis, wherein texts and phrases are listedin a top portion, and a graph of the polarity analysis in the bottomportion. For each expression or sentence, the polarity words areidentified. The positive words are indicated in green, while thenegative words are indicated in red. The number of words for eachpolarity type are then plotted over time. This information may be usedto identify positive or negative trends associated with release offeatures, upgrades, applications, services and so forth. The methodsdescribed hereinabove may be used to extract the topics and analyze thesentiment for generation of the information of FIG. 13.

The functions of the various modules and components of system 1200 maybe implemented in software, firmware, hardware, an Application SpecificIntegrated Circuit (ASIC) or combination thereof. A specific machine maybe implemented in the form of a computer system, within whichinstructions for causing the machine to perform any one or more of themethodologies discussed herein may be executed. In alternativeembodiments, the machine operates as a standalone device or may beconnected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine may be a Personal Computer (PC), a tablet PC, a Set-Top Box(STB), a Personal Digital Assistant (PDA), a cellular telephone, a webappliance, a network router, switch or bridge, or any machine capable ofexecuting instructions (sequential or otherwise) that specify actions tobe taken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a net (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The example computer system, such as system 1200, includes a processor,such as central processing unit 1218, which includes or executesinstructions for operations and functions performed within and by thecomputer system. Further, the memory storage 1208 may includeinstructions for storage in and control of memory storage 1208. A staticmemory or other memories (not shown) may also be provided. Similarly,the memory storage 1208 may be partitioned to accommodate the variousfunctions and operations within the system 1200.

The system 1200 may further include a video display unit (e.g., a LiquidCrystal Display (LCD) or a Cathode Ray Tube (CRT)) (not shown). Thesystem 1200 may also include an input device to access and receivecomputer-readable instructions from a medium having instructions forstoring and controlling the computer-readable medium.

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. A component may be any tangibleunit capable of performing certain operations and may be configured orarranged in a certain manner. In example embodiments, one or morecomputer systems e.g., a standalone, client or server computer system)or one or more components of a computer system (e.g., a processor or agroup of processors) may be configured by software (e.g., an applicationor application portion) as a component that operates to perform certainoperations as described herein.

In various embodiments, a component may be implemented mechanically orelectronically. For example, a component may comprise dedicatedcircuitry or logic permanently configured (e.g., as a special-purposeprocessor) to perform certain operations. A component may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) temporarilyconfigured by software to perform certain operations. It may beappreciated that the decision to implement a component mechanically, indedicated and permanently configured circuitry, or in temporarilyconfigured circuitry (e.g., configured by software) may be driven bycost and time considerations.

Accordingly, the term “component” may be understood to encompass atangible entity, be that an entity physically constructed, permanentlyconfigured (e.g., hardwired) or temporarily configured (e.g.,programmed) to operate in a certain manner and/or to perform certainoperations described herein. Considering embodiments in which componentsare temporarily configured (e.g., programmed), each of the componentsneed not be configured or instantiated at any one instance in time. Forexample, where the components comprise a general-purpose processorconfigured using software, the general-purpose processor may beconfigured as respective different components at different times.Software may accordingly configure a processor, for example, toconstitute a particular component at one instance of time and toconstitute a different component at a different instance of time.

Components can provide information to, and receive information from,other components. Accordingly, the described components may be regardedas being communicatively coupled. Where multiples of such componentsexist contemporaneously, communications may be achieved through signaltransmission (e.g., over appropriate circuits and buses) that connectthe components. In embodiments in which multiple components areconfigured or instantiated at different times, communications betweensuch components may be achieved, for example, through the storage andretrieval of information in memory structures to which the multiplecomponents have access. For example, one component may perform anoperation and store the output of that operation in a memory device towhich it is communicatively coupled. A further component may, at a latertime, access the memory device to retrieve and process the storedoutput. Components may also initiate communications with input or outputdevices, and can operate on a resource (e.g., a collection ofinformation).

Example embodiments may be implemented in digital electronic circuitry,or in computer hardware, firmware, software, or in combinations ofthese. Example embodiments may be implemented using a computer programproduct, e.g., a computer program tangibly embodied in an informationcarrier, e.g., in a machine-readable medium for execution by, or tocontrol the operation of, data processing apparatus, e.g., aprogrammable processor, a computer, or multiple computers.

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a module, subroutine,or other unit suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site or distributed across multiple sites andinterconnected by a communication network.

In example embodiments, operations may be performed by one or moreprogrammable processors executing a computer program to performfunctions by operating on input data and generating output. Methodoperations can also be performed by, and apparatus of exampleembodiments may be implemented as, special purpose logic circuitry,e.g., as a field programmable gate array (FPGA) or anapplication-specific integrated circuit (ASIC).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it may beappreciated that both hardware and software architectures requireconsideration. Specifically, it may be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor), or a combinationof permanently and temporarily configured hardware may be a designchoice. Below are set out hardware (e.g., machine) and softwarearchitectures that may be deployed, in various example embodiments.

As illustrated in FIG. 12, the machine-readable medium 922 of disk driveunit 916 stores one or more sets of instructions 925 and data structures(e.g., software) embodying or utilized by any one or more of themethodologies or functions described herein. The software may alsoreside, completely or at least partially, within the main memory 901and/or within the processor 902 during execution thereof by the computersystem 900, the main memory 901 and the processor 902 also constitutingmachine-readable media.

While the machine-readable medium 922 is shown in an example embodimentto be a single medium, the term “machine-readable medium” may include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore instructions or data structures. The term “machine-readable medium”shall also be taken to include any tangible medium capable of storing,encoding or carrying instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologiespresented herein or capable of storing, encoding or carrying datastructures utilized by or associated with such instructions. The term“machine-readable medium” shall accordingly be taken to include, but notbe limited to, tangible media, such as solid-state memories, and opticaland magnetic media. Specific examples of machine-readable media includenon-volatile memory, including by way of example semiconductor memorydevices, e.g., Erasable Programmable Read-Only Memory (EPROM),Electrically Erasable Programmable Read-Only Memory (EEPROM), and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions used within computer system 900 may further betransmitted or received over a communications network 926 using atransmission medium. The instructions, and other information, may betransmitted using the network interface device 920 and any one of anumber of well-known transfer protocols (e.g., HTTP). Examples ofcommunication networks include a local area network (“LAN”), a wide areanetwork (“WAN”), the Internet, mobile telephone networks, Plain OldTelephone (POTS) networks, and wireless data networks (e.g., Wifi andWiMax networks). The term “transmission medium” shall be taken toinclude any intangible medium capable of storing, encoding or carryinginstructions for execution by the machine, and includes digital oranalog communications signals or other intangible medium to facilitatecommunication of such software.

In some embodiments, the described methods may be implemented using oneof a distributed or non-distributed software application designed undera three-tier architecture paradigm. Under this paradigm, various partsof computer code (or software) that instantiate or configure componentsor modules may be categorized as belonging to one or more of these threetiers. Some embodiments may include a first tier as an interface (e.g.,an interface tier). Further, a second tier may be a logic (orapplication) tier that performs application processing of data inputtedthrough the interface level. The logic tier may communicate the resultsof such processing to the interface tier, and/or to a backend, orstorage tier. The processing performed by the logic tier may relate tocertain rules or processes that govern the software as a whole. A third,storage tier, may be a persistent storage medium, or a non-persistentstorage medium. In some cases, one or more of these tiers may becollapsed into another, resulting in a two-tier architecture, or even aone-tier architecture. For example, the interface and logic tiers may beconsolidated, or the logic and storage tiers may be consolidated, as inthe case of a software application with an embedded database. Thethree-tier architecture may be implemented using one technology or avariety of technologies. The example three-tier architecture, and thetechnologies through which it is implemented, may be realized on one ormore computer systems operating, for example, as a standalone system, ororganized in a server-client, peer-to-peer, distributed, or some othersuitable configuration. Further, these three tiers may be distributedbetween more than one computer systems as various components.

Example embodiments may include the above described tiers, and processesor operations about constituting these tiers may be implemented ascomponents. Common to many of these components is the ability togenerate, use, and manipulate data. The components, and thefunctionality associated with each, may form part of standalone, client,server, or peer computer systems. The various components may beimplemented by a computer system on an as-needed basis. These componentsmay include software written in an object-oriented computer languagesuch that a component oriented, or object-oriented programming techniquecan be implemented using a Visual Component Library (VCL), ComponentLibrary for Cross Platform (CLX), Java Beans (JB), Java Enterprise Beans(EJB), Component Object Model (COM), Distributed Component Object Model(DCOM), or other suitable technique.

Software for these components may further enable communicative couplingto other components (e.g., via various Application Programminginterfaces (APIs)), and may be compiled into one complete server,client, and/or peer software application. Further, these APIs may beable to communicate through various distributed programming protocols asdistributed computing components.

Some example embodiments may include remote procedure calls being usedto implement one or more of the above described components across adistributed programming environment as distributed computing components.For example, an interface component (e.g., an interface tier) may formpart of a first computer system remotely located from a second computersystem containing a logic component (e.g., a logic tier). These firstand second computer systems may be configured in a standalone,server-client, peer-to-peer, or some other suitable configuration.Software for the components may be written using the above describedobject-oriented programming techniques, and can be written in the sameprogramming language, or a different programming language. Variousprotocols may be implemented to enable these various components tocommunicate regardless of the programming language used to write thesecomponents. For example, a component written in C++ may be able tocommunicate with another component written in the Java programminglanguage through utilizing a distributed computing protocol such as aCommon Object Request Broker Architecture (CORBA), a Simple ObjectAccess Protocol (SOAP), or some other suitable protocol. Someembodiments may include the use of one or more of these protocols withthe various protocols outlined in the Open Systems Interconnection (OSI)model, or Transmission Control Protocol/Internet Protocol (TCP/IP)protocol stack model for defining the protocols used by a network totransmit data.

Example embodiments may use the OSI model or TCP/IP protocol stack modelfor defining the protocols used by a network to transmit data. Inapplying these models, a system of data transmission between a serverand client, or between peer computer systems, may, for example, includefive layers comprising: an application layer, a transport layer, anetwork layer, a data link layer, and a physical layer. In the case ofsoftware for instantiating or configuring components having a three-tierarchitecture, the various tiers (e.g., the interface, logic, and storagetiers) reside on the application layer of the TCP/IP protocol stack. Inan example implementation using the TCP/IP protocol stack model, datafrom an application residing at the application layer is loaded into thedata load field of a TCP segment residing at the transport layer. ThisTCP segment also contains port information for a recipient softwareapplication residing remotely. This TCP segment is loaded into the dataload field of an IP datagram residing at the network layer. Next, thisIP datagram is loaded into a frame residing at the data link layer. Thisframe is then encoded at the physical layer, and the data transmittedover a network such as an internet, Local Area Network (LAN), Wide AreaNetwork (WAN), or some other suitable network. In some cases, internetrefers to a network of networks. These networks may use a variety ofprotocols for the exchange of data, including the aforementioned TCP/IP,and additionally Asynchronous Transfer Mode (ATM), Synchronous NetworkArchitecture (SNA), Serial Data Interface (SDI), or some other suitableprotocol. These networks may be organized within a variety of topologies(e.g., a star topology), or structures.

Although an embodiment has been described with reference to specificexample embodiments, it may be evident that various modifications andchanges may be made to these embodiments without departing from thebroader spirit and scope of the present discussion. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense. The accompanying drawings that form a parthereof show by way of illustration, and not of limitation, specificembodiments in which the subject matter may be practiced. Theembodiments illustrated are described in sufficient detail to enablethose skilled in the art to practice the teachings disclosed herein.Other embodiments may be utilized and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. This Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it may be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, may be apparent to those of ordinaryskill in the art upon reviewing the above description.

What is claimed is:
 1. A system comprising: one or more hardwareprocessors; and a machine-readable medium for storing instructions that,when executed by the one or more hardware processors, cause the one ormore hardware processors to perform operations comprising: accessing adocument from a document record of a database; building a syntax tree ofa sentence of the document based on parsing the sentence; assigning apolarity impact to one or more words of the sentence based on matchingone or more lexical patterns and the one or more words of the sentence,a lexical pattern being a token sequence, each token of the tokensequence comprising a lemma field, a polarity tag field, and apart-of-speech tag field; determining that a plurality of words in thesentence have conflicting polarity based on the syntax tree and theassigned polarity impact of the plurality of words; determining aclassification of the sentence based on the determining that theplurality of words in the sentence have conflicting polarity; andgenerating a classification record of the classification in thedatabase.
 2. The system of claim 1, wherein the operations furthercomprise identifying one or more key phrases in the document based on afiltering technique.
 3. The system of claim 2, wherein the operationsfurther comprise identifying the sentence of the document based on theidentified one or more key phrases, the sentence including the one ormore key phrases.
 4. The system of claim 1, wherein the assigning of thepolarity impact to the one or more words of the sentence is furtherbased on a polarity word having a dominant impact on a topic.
 5. Thesystem of claim 1, wherein the assigning of the polarity impact to theone or more words of the sentence is further based on a sum ofpolarities method.
 6. The system of claim 1, wherein the assigning ofthe polarity impact to the one or more words of the sentence is furtherbased on a syntactic distance between the one or more words and a topickey phrase in the syntactic tree.
 7. The system of claim 1, wherein thedetermining of the classification of the sentence includes selecting apolarity classification for the one or more words in the sentence, theselecting resulting in classified one or more words.
 8. The system ofclaim 7, wherein the determining of the classification of the sentencefurther includes applying one or more heuristic rules to the classifiedone or more words and the sentence.
 9. The system of claim 1, whereinthe operations further comprise determining an opinion associated with adocument that includes the sentence based on the classification of thesentence.
 10. A method comprising: accessing a document from a documentrecord of a database; building, using one or more hardware processors, asyntax tree of a sentence of the document based on parsing the sentence;assigning a polarity impact to one or more words of the sentence basedon matching one or more lexical patterns and the one or more words ofthe sentence, a lexical pattern being a token sequence, each token ofthe token sequence comprising a lemma field, a polarity tag field, and apart-of-speech tag field; determining that a plurality of words in thesentence have conflicting polarity based on the syntax tree and theassigned polarity impact of the plurality of words; determining aclassification of the sentence based on the determining that theplurality of words in the sentence have conflicting polarity; andgenerating a classification record of the classification in thedatabase.
 11. The method of claim 10, further comprising: identifyingone or more key phrases in the document based on a filtering technique.12. The method of claim 11, further comprising: identifying the sentenceof the document based on the identified one or more key phrases, thesentence including the one or more key phrases.
 13. The method of claim10, wherein the assigning of the polarity impact to the one or morewords of the sentence is further based on a polarity word having adominant impact on a topic.
 14. The method of claim 10, wherein theassigning of the polarity impact to the one or more words of thesentence is further based on a sum of polarities method.
 15. The methodof claim 10, wherein the assigning of the polarity impact to the one ormore words of the sentence is further based on a syntactic distancebetween the one or more words and a topic key phrase in the syntactictree.
 16. The method of claim 10, wherein the determining of theclassification of the sentence includes selecting a polarityclassification for the one or more words in the sentence, the selectingresulting in classified one or more words.
 17. The method of claim 16,wherein the determining of the classification of the sentence furtherincludes applying one or more heuristic rules to the classified one ormore words and the sentence.
 18. A non-transitory machine-readablestorage medium comprising instructions that, when executed by one ormore hardware processors of a machine, cause the one or more hardwareprocessors to perform operations comprising: accessing a document from adocument record of a database; building a syntax tree of a sentence ofthe document based on parsing the sentence; assigning a polarity impactto one or more words of the sentence based on matching one or morelexical patterns and the one or more words of the sentence, a lexicalpattern being a token sequence, each token of the token sequencecomprising a lemma field, a polarity tag field, and a part-of-speech tagfield; determining that a plurality of words in the sentence haveconflicting polarity based on the syntax tree and the assigned polarityimpact of the plurality of words; determining a classification of thesentence based on the determining that the plurality of words in thesentence have conflicting polarity; and generating a classificationrecord of the classification in the database.